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All Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science Jurnal Wicara Desa
Iltizam, Muhammad
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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Classification of Diabetes Mellitus Sufferers Eating Patterns Using K-Nearest Neighbors, Naïve Bayes and Decission Tree Lubis, Ayuni Fachrunisa; Haq, Hilmi Zalnel; Lestari, Indah; Iltizam, Muhammad; Samae, Nitasnim; Rofiqi, Muhammad Aufi; Abdurrahman, Sakhi Hasan; Tambusai, Balqis Hamasatiy; Salsilah, Puja Khalwa
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 1: PREDATECS July 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i1.1103

Abstract

The study investigates three classification algorithms, namely K-Nearest Neighbor (K-NN), Naïve Bayes, and Decision Tree, for the classification of Diabetes Mellitus using a dataset from Kaggle. K-NN relies on distance calculations between test and training data, using the Euclidean distance formula. The choice of k, representing the nearest neighbor, significantly influences K-NN's effectiveness. Naïve Bayes, a probabilistic method, predicts class probabilities based on past events, and it employs the Gaussian distribution method for continuous data. Decision Trees, form prediction models with easily implementable rules. Data collection involves obtaining a Diabetes Mellitus dataset with eight attributes. Data preprocessing includes cleaning and normalization to minimize inconsistencies and incomplete data. The classification algorithms are applied using the Rapidminer tool, and the results are compared for accuracy. Naïve Bayes yields 77.34% accuracy, K-NN performance depends on the chosen k value, and Decision Trees generate rules for classification. The study provides insights into the strengths and weaknesses of each algorithm for diabetes classification
PENGOLAHAN UMBI GADUNG SEBAGAI BAHAN DASAR PEMBUATAN PRODUK LOKAL STICK GADUNG DI DUSUN BATU MELIK, DESA SEMINAR SALIT, KECAMATAN BRANG REA, KABUPATEN SUMBAWA BARAT Kartika, Amalinda; Sriminingsih, Orien; Sabriana, Ofhi; Muhammad, Syafiq; Hafiza, Nurul; Azzahra, Fatima; Maktun, Kintan Haiatul; Rahmatin, Ina Ramadhani; Ramadhoan, Ramadhoan; Fauzi, Ihsan; Sugiatma, Iksan; Iltizam, Muhammad; Milasari, Sri; Rozi, Fahrul; Priawibawa, Ghozi; Wardika, Ruzga; Afrida, Galuh; Andriani, Rini; Susanti, Elifah Rika; Sudiarta, I Wayan
Jurnal Wicara Vol 1 No 5 (2023): Jurnal Wicara Desa
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/wicara.v1i4.3391

Abstract

Dusun Batu Melik, Desa Seminar Salit, Kecamatan Brang Rea, Kabupaten Sumbawa Barat merupakan salah satu dusun yang berada di kawasan pegunungan yang kaya akan potensi hasil hutan berupa umbi gadung. Sejauh ini, potensi umbi gadung sangat melimpah namun hanya dimanfaatkan oleh masyarakat sebagai makanan pendamping yang jarang dikonsumsi. Umbi gadung belum dimanfaatkan secara maksimal oleh masyarakat meskipun potensi dan nilai tambah yang tinggi. Berdasarkan masalah tersebut, bersama mitra desa dilakukan pengolahan dan pengembangan hasil hutan ini menjadi produk lokal berupa stik gadung dengan konsep pemberdayaan Masyarakat. Tahap-tahap pelaksanaan program kemitraan berupa Identifikasi, perumusan dan solusi permasalahan; pelaksanaan Program; partisipasi Mitra dan evaluasi keberlanjutan program. Simpulan dari program pengabdian kepada masyarakat ini adalah: (1) Umbi gadung dapat memberikan nilai tambah bagi perekonomian masyarakat melalui pengolahan inovasi produk lokal salah satunya stik gadung. (2) Umbi gadung dapat diolah menjadi berbagai produk lokal, namun dengan proses pengolahan yang cukup lama untuk menghilangkan kandungan racun (HCN). Kegiatan pengolahan umbi gadung ini mendapatkan respon positif dari masyarakat Dusun Batu Melik. Program pelatihan pembuatan stik gadung ini menjadi salah satu opsi bagi masyarakat dalam hal pengembangan diri, olah keterampilan dalam upaya mewujudkan SDM yang berkualitas, terampil dan mandiri. Ditambah dengan adanya kegiatan penyuluhan keamanan pangan dan perizinan usaha yang sangat didukung dan direspon baik oleh masyarakat guna meningkatkan pengetahuan dan keterampilan dalam pengolahan makanan.
Analysis Comparison Classification Image Disease Eye Using the CNN Algorithm, Inception V3, DenseNet 121 and MobileNet V2 Architecture Models Melyani, Nasya Amirah; Lubis, Ayuni Fachrunisa; Tatamara, Aghnia; Haiban, Ryando Rama; Iltizam, Muhammad; Rofiqi, Muhammad Aufi; Abdurrahman, Sakhi Hasan; Samae, Nitasnim; Shahid, Bilal; Habibullah, Muhammad; Ismail, Muhammad Ibrara
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 1: PREDATECS July 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Eye disease is a significant global health problem, with more than two billion people experiencing vision impairment. Some of the main causes of visual impairment include cataracts, glaucoma, diabetic retinopathy, and age-related macular degeneration. Early detection of eye disease is very important to prevent blindness. The fundus of the eye, which includes the retina and blood vessels, is an important area in the diagnosis of retinal diseases. Fundus disease can cause significant vision loss and is one of the leading causes of blindness. Automated analysis of fundus images is used to diagnose common retinal diseases, ranging from easily treatable to very complex conditions. This research discusses eye disease image classification using several Convolutional Neural Network (CNN) architectures, namely Inception V3, DenseNet 121, and MobileNet V2. The dataset used is 4217 fundus images categorized based on the patient's health condition. Data is processed through normalization and augmentation to improve model performance. Experimental results show that MobileNet V2 has the highest accuracy of 81.3%, followed by Inception V3 with 77.3%, and DenseNet 121 with 76.7%. The use of appropriate CNN models in the classification of eye fundus images can help in early detection of eye diseases, thereby preventing further visual impairment.
Co-Authors Abdurrahman, Sakhi Hasan Afrida, Galuh Azzahra, Fatima Fahrul Rozi Fauzi, Ihsan Hafiza, Nurul Haiban, Ryando Rama Haq, Hilmi Zalnel I Wayan Sudiarta Indah Lestari, Indah Ismail, Muhammad Ibrara Kartika, Amalinda Lubis, Ayuni Fachrunisa Maktun, Kintan Haiatul Melyani, Nasya Amirah Milasari, Sri Muhammad Habibullah Muhammad, Syafiq Priawibawa, Ghozi Rahmatin, Ina Ramadhani Ramadhoan, Ramadhoan Rini Andriani Rofiqi, Muhammad Aufi Sabriana, Ofhi Salsilah, Puja Khalwa Samae, Nitasnim Shahid, Bilal Sriminingsih, Orien Sugiatma, Iksan Susanti, Elifah Rika Tambusai, Balqis Hamasatiy Tatamara, Aghnia Wardika, Ruzga